THE IMPACT OF OIL PRICE
FLUCTUATIONS ON THE
UKRAINIAN ECONOMY
by
Artem Myronovych
A thesis submitted in partial
fulfillment of the requirements for the
degree of
Master of Arts in Economics
National University of “Kyiv-Mohyla
Academy”
2002
Approved by __ ________________________________________________
Chairperson of Supervisory Committee
__________________________________________________
__________________________________________________
__________________________________________________
Program Authorized
to Offer Degree _________________________________________________
Date _________________________________________________________
National University of “Kyiv-Mohyla
Academy”
Abstract
THE IMPACT OF OIL PRICE
FLUCTUATIONS ON THE
UKRAINIAN ECONOMY
by Artem Myronovych
Chairperson of the Supervisory Committee: Professor Serhiy Korablin,
Institute of Economics Forecasting at Academy of Sciences of Ukraine
It is well known that Ukraine is underendowed in such strategic resources as
gas and oil, and taking into account that it specializes in the production of
machinery and chemicals, which are highly oil-energy consuming, Ukraine is
greatly dependent on the rest of the world. In order to satisfy all internal
needs it has to import crude oil from abroad. The goal of this paper is to
examine the dependence of Ukraine on the world oil market and to estimate
the impact of possible changes in oil prices on main Ukrainian economic
indicators, such as output, unemployment, and general price level. Using an
approach proposed by Gisser and Goodwin (1983) it can be shown that oil
price fluctuations affect Ukrainian GDP and inflation, but have no effect on
unemployment at different lags at any statistically significant levels. The
results of Error Correction Model indicate that Ukraine will likely face a loss
in nominal GDP equals approximately $215 mln during the first quarter as a
response to a 20% increase in the price of oil. This result is close to that
obtained by IMF (2000), taking into account that they simulated longer
period. Indicators monetary and fiscal policies both have the expected signs
and positively affect GDP, but they are not a matter for unemployment. A
detailed explanation for this result is also presented. As economic theory
suggests, monetary policy has greater effect on the main economic indicators
than fiscal policy. The thesis then provides possible explanation of the
obtained results and their economic meaning. Finally, it gives the description
of possible policy implications and the direction for further studies.
TABLE OF CONTENTS
List of figures ......................................................................................................................ii
List of tables .......................................................................................................................iii
Acknowledgments.........................................................................................………….iv
Glossary................................................................................................................................. v
Introduction ......................................................................................................................... 1
Chapter I. Literature review……………… ................................................................5
1.1. Oil shocks issue in modern economy ............................................................... 5
1.2. Approaches to estimation impacts of oil shocks ............................................ 6
Chapter II. Theory and methodology ...........................................................................10
2.1. Description of oil industry in Ukraine............................................................10
2.2. Government regulation .....................................................................................18
2.3. Correlation between oil prices and macroeconomic performance ...........21
Chapter III. Empirics and results...................................................................................29
3.1. Data description..................................................................................................29
3.2. Methodology…………….. .............................................................................29
3.3. Analysis of results ...............................................................................................31
Chapter IV. Discussion....................................................................................................39
Conclusions ........................................................................................................................45
Works Cited........................................................................................................................46
Appendix 1: Prices of oil and inflation in Ukraine ....................................................49
Appendix 2: ADF test for series stationarity ...............................................................50
Appendix 3: GDP regression. Residuals diagnostic tests .........................................51
Appendix 4: Inflation regression. Residuals diagnostic tests ...................................52
Appendix 5: Unemployment regression. Residuals diagnostic tests ......................53
LIST OF FIGURES
Number
Page
Figure 1. Consumption and production of oil in Ukraine………………… 10
Figure 2. Market shares of Ukrainian oil refineries, 2001, % …………….. 12
Figure 3. Origin of oil supply to Ukraine ……………………………… . .13
Figure 4. Oil Refining Industry Concentration (1999-2001) ……………… 14
Figure 5. Real GDP and the price of oil in Ukraine …………………….. 24
Figure 6. The effect of temporary and permanent fiscal expansion ……… 25
Figure 7. The effect of temporary and permanent monetary expansion . …26
ii
LIST OF TABLES
Number
Page
Table 1. Regression estimates ……………………………………………31
Table 2. The scale of Russian export duties for crude oil..……………. …..40
iii
ACKNOWLEDGMENTS
The author wishes to express his deep gratitude to his thesis advisor Professor
Michael Hemesath for his valuable comments and suggestions during
research. The author also thanks Pr. Roy Gardner, Pr. Stefan Luts and Pr.
Ghaffar Mughal for their assistance and criticism. Special thanks to Pr. Iryna
Luk’yanenko for her timely notes on econometrical part and Marianna
Kudlyak, EERC MA student, for her support.
iv
GLOSSARY
Barrel (bbl). A unit of capacity usually used to measure oil. Contains
approximately 159 liters or 35 gallons. Conversion factor from barrels to tons
fluctuates from 7 to 9 barrels per ton depending on oil density.
Druzhba (friendship) and Pridneprovskie Magistral’nye Nefteprovody
(pridneprovsk trunk pipelines). Compose a system of two separated
Ukrainian pipelines joining Russia with the Western Europe with total extent
of 3850 km and total output capacity of 67.2 mln. tons of oil annually.
Naftogas of Ukraine. National oil and gas state-run company, created in
1998 in order to control the domestic market.
OPEC. Organization of Petroleum Exporting Countries.
v
INTRODUCTION
The oil shocks over the last few decades have had a profound effect on the
growth prospects of both the oil-exporting and oil-importing countries.
Taking into consideration the ever-growing needs for oil and gas in order to
satisfy production, it is clear that countries that lack these resources are highly
dependent on the rest of the world and are more sensitive to economic
performance fluctuations.
In the fall of 1973 the whole world economy faced an energy crisis after
OPEC countries almost tripled prices for crude oil. Since this organization
accounts for about 60 % of world oil trade and about 40 % of world
production, the impact of its price increases on the world economy was
significant: the majority of oil-importing countries faced declines in economic
growth; output and exports in many countries decreased significantly
accompanied by rising inflation and unemployment, giving rise to the
phenomenon of stagflation. Numerous studies were carried out to investigate
possible effects of oil price fluctuations on the main economic indicators of
oil-importing countries. Hamilton (1983) was the first to report the
econometric correlation of oil shocks with U.S.A. business cycles and GDP
growth. Later a strong relationship was found in many countries between oil
price shocks and the levels of investment and inflation (Gisser and Goodwin,
1986), employment (Haltiwanger, 1999) as well as the trade balance (IMF,
2000).
It is well known that Ukraine is underendowed in such strategic resources as
gas and oil. Taking into account its major export products (metal and
chemicals), which are highly energy intensive, Ukraine is greatly dependent on
energy sources. Over the period 1991-2001, Ukraine maintained the oil
production at approximate 90 thousand barrels per day, which satisfies only
25% of internal needs. Ironically, Ukraine exports some refined products,
1
while the domestic agricultural sector cannot find the fuel to ensure sowing in
spring (the volume of export is not very large). In order to provide the
economy with the necessary amount of oil, Ukraine has to import the rest
(nearly 75%) from abroad. The share of oil and refined products costs in total
import costs is about 40% and contributes approximately $5 bln. annually
(about 13% of 2001 nominal GDP)1. Oil is mainly imported from Russia and
Kazakhstan (however, it should be underlined that Russia can affect the
amount of imported oil from Kazakhstan by imposing quotas, because the
pipeline belongs to the former). Several years ago, Russia raised the price for
export products. Ukraine, which had no alternative source to import from,
suffered as a result. On the other hand, Russian prices for oil are strongly
affected by OPEC prices. As a result, the Ukrainian economy is tightly
connected with world prices for energy resources.
The goal of my thesis is to analyze the impact of possible changes in world oil
prices on the main Ukrainian economic indicators, such as GDP,
unemployment, and inflation. To understand the importance of the impact
of raised prices, let us recall recent military actions in the Persian Gulf, when
Iraq’s troops occupied Kuwait and seized its oil deposits. The result is well
known – the overall price of crude oil rose dramatically accompanied by a
crisis at leading stock markets. Taking into consideration that Ukraine’s
exports are mostly concentrated in metallurgy, machinery and chemicals
(about 50%) which are the main consumers of oil, it is obvious that there is a
strong connection between prices for fuel resources and overall Ukrainian
well-being. Thus, it could be easily predicted that the price increase would
cause significant decline in GDP as well as employment
Recent studies, conducted by IMF (2000) investigate the impact of $5 per
barrel increase in oil price on the performance of transition countries. They
applied a cross-country MULTIMOD simulation model, and found out that
1
IMF (2001) “Ukraine: Statistical appendix”
2
Ukraine will face a 1.4% decline in GDP ($390mln)2 due to first round effects.
However, this result is under considerable doubt, since in the simulations the
2001 oil price was assumed to be constant at $23 per barrel while the actual
price remained highly volatile due to the subsequent OPEC behavior and to
recent terrorist attacks in America (with more then $26 per barrel on average
before September 11 and about $18 after the attacks). Such dramatic fall in
price (30%) was caused by several reasons. First, decline in demand for
aircraft fuel by air companies (by 20%)3 in response to decrease in demand for
air flights. Second, by overall slowdown of economic growth in the USA, who
is known to be the largest consumer of crude oil in the world (the United
States consume 27% of the world consumption). Third, rather warm winter in
some American States. It was noted that oil consumption is subject to
seasonal patterns (peaks of consumption are observed in 4th quarter with
troughs in 2nd quarter4. The volume of heating oil consumed by the USA is
increasing over the last years, so it seems reasonable that demand for fuel falls
as it becomes warmer.
In this paper I use a slightly improved model proposed by Gisser and
Goodwin (1986). They estimate St. Louis - type equations of four indicators
of macroeconomic performance, namely, real GDP, general price level, rate
of unemployment and real investment; while oil prices, money supply, and
high-employed budget expenditures serve as exogenous variables. They
empirically tested their model for the U.S. economy, and found that a 10%
increase in oil price growth rate causes a 1,1% decline in real GDP growth
rate as well as a 0,86% decrease in general price level during the first 12
months. For the Ukrainian case these figures are expected to be higher
because of great dependence on oil, terms of trade effects (Ukraine does not
export crude oil), and due to the absence of appropriate policy response. In
the absence of international assistance, the inability to operate in private
2
In fact, nominal Ukrainian GDP in 2001 amounted UAH 191250 bln and 1.4% constitutes UAH
2677.5 bln or $505 mln if the value in Hryvnas is divided by average exchange rate.
3
“Troyka-dialog”, monthly report, February 2, 2002
4
IMF (2000); “The impact of higher oil prices on the global economy”
3
capital markets is likely to make the impact of oil shocks greater, primarily due
to a reduction in domestic demand.
Investigating the impact of oil price fluctuations on the Ukrainian
macroeconomy can serve as the key to understanding the formation of
macroeconomic parameters which predetermine social and economic
development. Moreover, government policies, which can smooth the negative
impacts, will be more efficient in the light of the results. Many studies support
the idea that monetary policy is the best tool to cope with undesirable effects
of higher oil prices. For example, Gisser and Goodwin (1986) suggested in a
statistically valid manner that fiscal policy was insignificant in the U.S. over
the last 25 years. On the other hand, monetary policy turned out to be
significant in the first period and then its effect gradually disappeared over
time. The results of many studies are based on the presumption that monetary
policy rather than fiscal policy is the powerful tool to smooth fluctuations due
to oil shocks.
4
Chapter 1
LITERATURE REVIEW
Since the aim of my work is to investigate the relationship between oil price
fluctuations and such macroeconomic indicators as GDP, unemployment,
wages, export and output, in this chapter I attempt a brief overview of the
modern economic treatment of this connection and present its results.
1.1. Oil shocks issue in modern economy
Many researchers agree in opinion that no other economic event in post
World War II era generated as much attention as the series of oil price
shocks, mainly produced by OPEC countries. No studies were necessary to
see the clear relationship between oil prices and main economic indicators.
Nevertheless, this issue was new and researchers posed such a question as
the numerical impact of oil shocks and their correlation with the policy
conducted by government in order to predict the best instrument to cope
with the negative impacts caused by oil price increases. Since then a large
number of studies have reported a correlation between increases in oil prices
followed by economic downturns. Examples include Fried and Schultz
(1975), Rasche and Tatom (1977,1981), Burbridge and Harrison (1984), Mork
(1989), and many others.
Particularly, Hamilton (1983) investigated the impact on the US economy.
His evidence suggests that crude oil prices have a strong relationship with the
US business cycle and tends to highlight cost-push inflationary effects, while
the research of Berndt and Wood (1975,1979) as well as Wilcox’s (1983)
indicates the complementarity between energy prices and capital in the US
economy is rather strong, both before and after 1973. Hence, oil price shocks
may have a stronger effect than generally believed. These results were later
extended by Mork (1989) and Hooker (1999) who argued that asymmetric
5
and nonlinear transformations of oil prices restore that relationship, and thus
the economy responds asymmetrically and nonlinearly to oil price shocks.
Later Hamilton (2000) reported
clear evidence of nonlinearity-oil price
increases are much more important than oil price decreases. An alternative
interpretation was proposed based on the estimation of a linear functional
form using exogenous disruptions in petroleum supplies as an instrument.
His study shows that oil shocks play a crucial role in determining
macroeconomic behavior because they disrupt spending by consumers and
firms.
1.2. Approaches to estimation of impacts of oil shocks
One approach to investigate this relationship was presented in “Crude Oil
and the Macroeconomy: Tests of Some Popular Notions”, (1986), in which
M.Gisser and T. Goodwin tried to summarize results based on the previous
research. They formally tested three hypothesis associated with the 1983
energy crisis:
1. That the impact of oil price shocks is largely in the form of cost-push
inflation.
2. That crude oil prices affected the macroeconomy very differently before
and after 1973.
3. That crude oil prices are determined very differently under the post-1973
institutional regime than under the pre-1973 regime.
In my work I will analyze the possible relationship starting from 1991, so the
third hypothesis is not of interest. To check the remaining hypotheses the
authors employed a reduced-form approach for US economy. One approach
to investigating the first notion is to estimate St. Louis- type equations of four
indicators of macroeconomic performance, namely, real GDP, general price
level, the rate unemployment and real investment. Each equation takes the
form:
6
4
4
4
i =0
i =0
i =0
Χ t = α 0 + ∑ mi M i −1 + ∑ f i Fi −1 + ∑ oi POi −1 + ε t
where Xt is the indicator of macroeconomic performance, Mt-1 is the money
supply (M1B), Fi-1 is the high employment federal expenditures measure of
fiscal policy and POi-1 is the real price of crude oil. The impact of monetary
and fiscal policies on real GDP turned out to be statistically insignificant,
while a 1 % change in oil price reduces GDP by 0.11 % with 5 % level of
significance. The results for the price deflator are very different. Here
monetary policy dominates in size, while 1% change in oil price causes the
price increase by only 0.086 %. However, comparing the contribution to R2,
they found out that oil price contributes approximately 55%
in both
regressions. The same feature was observed in the next regression, where oil
price change leads to a 0.69% increase in the unemployment rate and to a
0.32 % decrease in investment in the first-round effect and 0.32% and -0.25%
in following years respectively. So, the authors concluded that crude oil prices
have had a significant impact on a broad range of macroeconomic indicators,
often exceeding that of monetary policy and always exceeding that of fiscal
policy.
Although many studies have been devoted to the impact of oil price
increases, rather less attention has been paid to the role of monetary policy as
a possible remedy. A recent paper by Bernanke, Gertler and Watson (1997)
suggests that monetary policy could be used to eliminate any recessionary
consequences of an oil price shock. They estimated a VAR model, describing
yt , which contains monthly series for the rate of growth of GDP in real
terms (γGDP, t), the log of the GDP deflator (γDEF, t), log of commodity price
index (γCOM, t), a measure of oil prices (γOIL, t), the Fed funds rate (γFED, t),
the 3-month treasury bill rate (γTB, t), and the 10-years Treasury bond rate
(γT10, t) of the form:
B0yt= k0+B1yt-1+B2yt-2+B2yt-2+…+Bpyt-p+vt
The matrix B0 is taken to be the singular triangular and Fed funds rate affects
the first four variables by assumption. The model, based on the data set
7
1965:1-1995:12 was estimated by OLS with the lag length was set to 7. By
simulating the equation they determined the effect of 10% increase in the net
oil price on each value of yt+s . This change would result in 0.25% slower
GDP growth and 0.2% higher prices after two years, while the Fed rates rise
80 points during the first year. The result raises the question whether the
slowdown was caused by oil shocks or by the rise in the interest rate. This
issue was thoroughly discussed by Lucas (1976) as well. Actually, an increase
in money supply has two effects. Since money will become more liquid, the
nominal interest rate must fall, while a new cycle of inflation increases the
rate. The authors included this feature while using Sims-Zha method to
estimate the value of historical shocks. Again, by simulating, they found that
a 10% increase in oil price leads to downturn in GDP growth. Repeating the
same simulation with policy shocks they determined that Fed funds rate
should be rising starting at 4%.
Another point of view on the role of monetary policy as a response to an oil
shock was proposed by J. Hamilton (2000). He tested two hypotheses:
whether the Fed has the power to implement such a policy and whether the
size of effect that Bernanke, Gertler and Watson attributed to oil shock as
large as predicted. Using the same technique and methodology (Sims-Zha
approach) he came to the conclusion that oil shocks have a bigger effect on
the macroeconomy then was predicted earlier. He suggested choosing 12 lags
instead of seven as did Bernanke et al. (1997). Hamilton’s proposal could be a
good answer to Bohi’s question (1989) of whether the recessions that
followed the big oil shocks were caused not by the oil shock themselves, but
rather by the Fed’s response.
The next approach which could be useful in analyzing the abovementioned
relationship was highlighted by Hunt, Isard and Laxton (2001). They used
MULTIMOD (multi-regional macroeconomic model developed by the IMF
staff for the primary purpose of analyzing alternative scenarios for the World
Economic Outlook) to investigate the macroeconomic effects of oil price
shocks, distinguishing between temporary and permanent shocks. This model
8
is based an annual data and takes the WEO forecast as an exogenous
baseline; it was designed to avoid first-order Lucas-critique problems and to
shed the light on the main role of monetary policy response. The key
equations in MULTIMOD model take the reduced-form structure:
πtCPI = δ1 πtM + δ2 πtC + δ3 πtPOIL + [1- δ1- δ2- δ3] π CPI t-1
(1)
πtC = Ψ πet+1 + [1-Ψ] πt-1C + γ[(ut*-ut)/( ut-φt)] +α[π CPI t-1 - πt-1C ] (2)
πet+1 = Ω[λ π CPI t+1 +(1- λ) πt+1C ]-[1- Ω][ λ π CPI t-1 +(1- λ) πt-1C ] (3)
where πtCPI is CPI inflation; πtM is the rate of the domestic-currency price of
manufactured imports; πtPOIL is the rate of inflation of the domestic –currency
price of oil; πtC is the core inflation; πe is an expected inflation; u* is the nonaccelerating-inflation rate of unemployment (the NAIRU); u is the
unemployment rate; φ is the minimum absolute lower bound for the
unemployment rate. MULTIMOD simulations are used to compare the costs
of two possible types of policy errors in responding to a persistent increase in
oil prices. The first error results from policymakers assuming that oil price
increases will have no inflation effects; the second is that agents are assumed
to respond in a more inflationary manner. The results of these simulations
are: first, experience during 1980-1990 does not provide a valid basis for
dismissing the risk that persistent oil-price increases will pass through into
core inflation. Second, delay in responding to a persistent oil-price increase
can have large macroeconomic costs if it leads to an erosion of monetary
policy credibility. Third, policymakers should interpret the data in a manner
that errs in the direction of a more aggressive policy response to oil-price
increases (Hunt et.al.2001). This research has found clear correlation between
oil prices and aggregate measures of macroeconomic activity, as well as
significant correlations between oil prices and
macroeconomic data on
output, employment, and real wages. In addition, there is a strong evidence
of asymmetry in the relationship between oil price changes and
corresponding changes in economic activity.
9
Chapter 2
THEORY AND METHODOLOGY
In this chapter I will provide the description of the Ukrainian oil industry,
showing the importance of energy resources for the Ukrainian
macroeconomy and will show the dependence on Russia as the main
supplier of crude oil. In the last section I will run an economic analysis
why oil shocks could matter for the economy.
2.1. Description of oil industry in Ukraine
There are three petroleum producing regions in Ukraine (the Carpathian
region, Dnipro-Donetsk region and the Black sea and Crimea), but they
produce approximately 20-25% of the internal consumption. There are more
than 8000 oil and gas fields in Ukraine which are estimated to contain 7-8
billion tons of crude fuel5.
Figure 2.1. Production and consumption of oil in Ukraine
Average production of oil has been stable over the last few years and amounts
4 million tons per year. In 1975 Ukraine produced about 14 million tons, so
we observe a sharp decrease, mainly due to deterioration of equipment and a
10
decline in industrial consumption which was caused by numerous enterprises
closures. In fact, Ukraine's energy sector is now plagued by increasing foreign
debt for oil and gas, outdated and inefficient equipment, fuel shortages, barter
deals, and non-payment by consumers. Moreover, Ukrainian oil is of lowquality and costs of pumping are high compared to other countries, so it
seems reasonable to import rather than to explore new deposits.
Ukrainian oil stocks are not sufficient to ensure all internal needs. In order to
provide the economy with the necessary stock, Ukraine has to import it. The
share of imported oil, delivered to Ukrainian petroleum refineries in August
2001 was about 90%6. Thus, oil markets, which set the price for crude oil in
Ukraine should be divided into the domestic one and the rest of the world.
The lion’s share of oil is imported from Russia—about 66% (August, 2001)
and approximately 22% is delivered from Kazakhstan7. Four out of six
refineries in Ukraine are owned by Russian and Kazakhstanian companies; in
turn, these are regulated by respective governments.
The process of expansion in oil refining and shrinking in oil production led to
significant shifts in crude oil supply to domestic oil refinery plants. While in
2000 Ukraine supplied 35% of crude oil, the crude oil supply in 2001 shrunk
by half to 17%. At the same time the shares of Kazakhstan and, particularly,
Russia have markedly grown and are likely to grow further. It is important to
say that the origin structure of oil supply is not determined by economic
factors only.
Figure 2.2. Origin of oil supply
2000
5
6
7
2001(10m)
Source: US Embassy Kyiv (2000)
Business, September 17, 2001,Vol. 453, pp.28.
IMF Annual Report, 2001
11
Ukraine
17%
Kazakhstan
22%
Kazakhstan
26%
Ukraine
35%
Russia
43%
Russia
57%
Source: http://oilreview.kiev.ua
Other factors underlying the supply structure as well as other peculiarities in
domestic market will be covered in the following section.
There are three oil markets in Ukraine that can influence the internal oil price:
the market of oil extraction, the market of oil refining and the market of oil
trading. Since the first is fully controlled by the state (approximately 96% of
all domestic oil is produced by Naftogas, a Ukraine state-run company, a state
monopoly which controls almost all of oil production. In the refining industry
it resembles an oligopoly well – there are limited number of refineries (six)
and there are natural barriers to enter, because existing refineries could
potentially produce twice as much as is consumed in Ukraine and the costs of
construction of the new refinery are enormous. Moreover, four refineries are
owned by foreigners who produce approximately homogeneous products,
and currently they are engaged into price competition policies. As a result,
prices for refined products have fallen over the last 6 months. Finally, the
number of oil traders in Ukraine is large enough, but there are barriers to
enter as well (such as price of a license or special requirements). So, I can
draw a conclusion about the monopolistic competition in domestic oil trade
market. Despite the level of state control, there are a number of successfully
operating companies with foreign investment in oil and gas extraction.
Mainly, they are small foreign companies which work in joint ventures or
under joint agreement with Ukrnafta or with the enterprisers subordinate to
the Geology Committee of Ukraine.
The figure below shows the market shares of oil refineries in Ukraine.
12
Figure 2.3. Market shares of Ukrainian oil refineries in 2001, %
Lisichansky
NPZ
33%
Krem enchuzky
NPZ
27%
Nadvirniansky
Drohob ytsky
NPZ
NPZ
6%
7%
Odesky NPZ
15%
Khersonsky
NPZ
12%
Source: : "Business" # 47, 2001
Three major players, TNK (Tumen Oil Company, Russia, possesses
Lisichansky
Oil
Refinery),
Joint
Venture
UkrtatNafta
(possesses
Kremenchuzky oil refinery), and LukOil-Ukraine (a subsidiary of LukOil,
Russia, possesses Odesky oil refinery)
have been pursuing the strategy of
price wars to gain their market share. The current market shares are estimated
to constitute 33%, 25% and 15%, respectively. Thus, Russia controls over the
2/3 of Ukrainian market. The targets announced are approximately 40%, 30%
and 20 % by the aforementioned companies.
These figures support the conclusion of Bertrand-like oligopolistic
competition. According to this model, producers are undercutting their price
a bit lower then rival’s one to gain market share. ORPs were perusing exactly
the same strategy, yielding the significant decrease in wholesale and retail
decrease of gasoline and other refined products. For example, average Diesel
fuel prices decreased by 36-39% since 12’00, А-76 — by 34-38%, А-92 by 2933%, А-95 — by 27,3-29,6%8. According to reports by market dealers9, in 4-6
months these ORPs’ prices would reach their marginal costs, but still would
make profits due to price differential of raw materials in Russia and Ukraine.
8
Source: DerzhKomStat, author’s calculations
9
http://www.dsnews.com; last accessed on January 14, 2002
13
To measure the competitive background in the oil refinery industry I
employed Herfindahl-Hirshman Index (HHI). As can be seen in Figure 2.4
over the last three years industry concentration decreased slightly from
HHI=3115 in 1999 to HHI=2311 in 2001. This change is attributed to the
entry of Russian oil companies in the Ukrainian market and more efficient use
of oil refinery plants. In spite of the reduction in concentration, the Ukrainian
oil refinery industry is still classified as highly concentrated10 (HHI>1800)
which in turn allows market players to engage in activities designed to push
their rivals out of the market (e.g. trade wars) and means that any changes in
the industry structure may greatly affect competitive background.
Figure 2.4. Oil Refining Industry Concentration (1999-2001)
3500
3115
3000
HHI
2500
2155
2311
2000
2001(10m )
2000
1500
1000
500
0
1999
Source: http://oilreview.kiev.ua
Over the year 2001 the industry has also remained highly concentrated. At the
same time, the volume of oil processed has significantly increased from 5.34
m tons in January to 17.48 m tons in October (more than thee times.)
The present level of consumption of oil in Ukraine is about 30-35M tons a
year with the potential growth horizon is about 50M tons annually and the
bottom horizon for the amount of oil demanded is 23M. tons. Major
categories of consumers that have influence on oil demand:
1.Industrial consumers:
10
http://www.investopedia.com/terms/h/hhi.asp
14
-chemical enterprises
-oil-processing companies
-transport companies (auto tracking companies, Ukrzaliznytsya, river and
sea shipping companies)
2.Private consumers
-automobile owners.
The causes of decline in demand for oil lie primarily in
•
low receivables collection by traders
•
low solvency of industrial and private consumers.
Ukraine’s exports are mostly concentrated in metallurgy and machinery
(about 50%) – industries whish are also the main consumers of imported oil.
In order to analyze the impact of oil pricing on Ukrainian total output we
should take into account main domestic plants and factories. On the other
hand, Russian oil firms such as LUKoil or TNK should be included in
analysis as well, because these companies determine oil pricing (in accordance
with Russian and Ukrainian Legislation). Oil and gas exports are the main
source of income in Kazakhstan, so this industry is supported by the
Government there. Their domestic oil companies are supported by state in
the form of tax relief and low-interest rate loans. Hence, foreign companies
should be treated as actors in the analysis.
Ukraine does not import crude oil from OPEC countries, because of higher
prices and enormous transportation costs. Despite that, Ukraine has the
opportunity to import oil by sea (Odessa port recently became a free
economic zone and has enough capacity to ensure Ukrainian needs). There
are also Russian-Ukrainian oil pipelines which reduce the costs of
transportation. This system of oil transportation consists of two major
pipelines:
Druzhba
(friendship)
and
Pridneprovskie
Magistral’nye
Nefteprovody (Pridneprovsk trunk pipelines). They compose a system of
two separated Ukrainian pipelines joining Russia with Western Europe with
3850 km of total pipe length and total output capacity of 67.2 mln. tons of oil
annually. Ukraine plays an important role as a transit country for Russian oil
15
exports to Europe. The southern branch of the 1.2-million-bbl/d Druzhba
pipeline from Russia transits Ukraine en route to Slovakia, Hungary, and on
to Western Europe. In addition, due to its geographic location and its oil
pipeline system, Ukraine has an excellent opportunity to play a major role in
bringing increased oil exports from Azerbaijan and Kazakhstan to European
oil markets. Rather than seeking to import Caspian Sea oil for domestic
consumption, Ukraine is hoping to reap tariffs for Caspian oil transiting its
territory as it heads westwards11.
The chief components of Ukraine's strategy are the $750-million Pivdenny oil
terminal and the 500,000-bbl/d Odessa-Brody pipeline. Ukraine is hoping to
entice Caspian oil exporters shipping oil via the Black Sea to bypass the
crowded Bosporus Straits, already a major chokepoint for tankers, and instead
send their oil to European markets via Ukraine. However, Ukraine has not yet
found any oil companies to fill the pipeline, and the country's attempts to
make itself more attractive to investors--by stepping up oil sector privatization
efforts or by proposing an international consortium to manage the pipeline –
have seen only limited results thus far.12
It also should be taken into account that Russia recently (in the fall of 2001)
refused to join OPEC, since experts believe that in five years 75% of Russian
oil will be in the hard-extractive category (i.e. costs of pumping will increase)
and Russia will not be able to set lower prices at OPEC request, thus making
the oil industry a loss-maker. Taking into account that domestic demand for
crude oil is inelastic (i.e. oil and gas are necessities for the economy ), Ukraine
is greatly dependent on Russia who can increase their price up to the point,
where Ukraine would switch its partnership in favor of OPEC (taking into
account transportation costs). Hence, Russia cannot set the price for crude oil
much higher than OPEC’s, else Ukraine will import stock from the Persian
Gulf.
11
Source: http://www.eia.doe.gov
12
ibid.
16
Today Ukraine is facing more difficulties in attracting petroleum investments
than its neighbours. It has never been a large producer and is perceived to
have small fields which are hard to find and develop. To implement market
oriented oil and gas services and attract foreign investments, new legislation
was enacted (The Law on Concessions and the Law on Producing Sharing
Agreements). Government institutions were reformed, licensing and tax
policies were liberalized and a series of oil and gas development projects were
offered for implementation. In accordance with different estimates in order
to modernize the oil industry in Ukraine during the coming five years, it is
necessary to invest in it about $ 30-50 bln. The priority directions of
investments are the following:
-
Fuels and lubricants;
-
Pipeline construction equipment;
-
Oil and gas drilling machinery and technologies;
-
Equipment for atmospheric-vacuum oil refining;
-
Cracking units, distillation units
-
Industrial automation, control and monitoring systems for
refineries, gas processing and petrochemical plants;
-
Desulphurization and quality control facilities;
-
Safety systems;
-
Fuel storage and dispender system;
-
Fuel level monitoring and accounting system.
Unfortunately, Ukraine cannot cope with these investment levels alone, so it
needs to attract foreign investments. Now four out of six petroleum refineries
in Ukraine belong to Russian and Kazakhstanian oil companies, which are
obliged to invest approximately $1.5bln each in the Ukrainian refinery
industry in order to maintain the production. Today Canadian, British and
Russian companies are exploring new deposits in Crimea and the DniproDonetsk region. In addition, Ukrtransnafta ( the domestic oil transportation
monopoly) is going to sign a contract with Kazakhstan about the
17
transportation of Kazakhstanian oil and gas to Europe through OdessaBrody pipeline. These demonstrate a positive shifts in
the process of
attracting investments to oil and gas industry over the last few years.
2.2. Government regulation
The following is a brief description of the Ukrainian legislative base, which
determines the prices for stock oil and the amount to be imported. In the
context of this thesis institutions are rules, enforcement mechanisms, and
organizations supporting market transactions, which exist in Ukraine. They
help to analyze information, determine property rights and manage
competition. These institutions predetermine the development of the
Ukrainian economy, since the latter is strongly dependent on imported oil
and appropriate laws and decrees will allow rational and successful economic
activity.
Naftogas of Ukraine is a national holding company which was created in early
1998 to control and manage all state-owned shares of oil and gas companies.
It is the largest company in Ukraine in this sector and its subsidiaries control
oil and gas transportation through the pipelines. This company and its
branches Ukrgasvydobuvannya, Ukrnafta and Chornomornaftogas, produce about
97% of domestic natural gas and 94% of oil. Naftogas owns all oil and gas
transportation systems, and most oil and gas storage facilities in Ukraine. In
accordance with the Ukrainian legislation, these facilities cannot be privatized,
but in the future some part of them are intended to be privatized. Naftogas is
a vertically integrated company that performs several functions: oil and gas
production
and
exploration
(Ukrgasvydobuvannya,
Ukrnafta
and
Chornomornaftogas), gas transportation and storage (Ukrtransgas), gas trading
(Trade House Gas of Ukraine), oil transportation (Druzhba pipeline
company), oil refining (Azmol) and liquefied petroleum gas (LPG)
transportation (Ukrspetstransgas).
On August 15, 2001 the President signed the Law “About oil and Gas”
adopted by Verchovna Rada. This document fixes the rules of activity of
18
Ukrainian oil companies which are not now allowed to set prices for
imported crude oil higher than those stipulated by the Government. In
addition, it predetermines the activity of the antimonopoly Committee in the
fuel sector and sets a price ceiling for refined petroleum which is delivered to
factories by domestic refineries. A bit earlier, the Verchovna Rada adopted a
set of tariffs and duties for imported and exported crude oil and fuel
products. In order to avoid price increases in the domestic market, the
procedure for importing was simplified and tariffs were set at an optimal
level, while it became too expensive to export During the 1998-1999 session
new legislation was enacted - the Law on Concessions and the Law on
Production Sharing Agreement that reformed government administrative
institutions; licensing and tax policies were liberalized and a number of
development projects were offered for implementation.
The main goals that the Ukrainian government is trying to achieve through
the industry regulation are:
•
ensure a stable supply of petroleum products for domestic producers
and consumers;
•
ensure socially optimal distribution of oil products;
•
ensure a high level of revenues for the budget;
The tools available for regulation the industry can be divided into direct and
indirect. The direct tools are:
•
tariffs on import of crude oil and oil products;
•
tariffs on crude oil and oil products transportation via trunk pipelines;
•
licensing of crude oil and oil products transportation via trunk
pipelines;
•
restriction of enterprises authorized to produce gasoline;
•
tariffs on oil extraction (UAH 17.34 per ton);
•
international treaties (they may provide exemptions from duties and
taxes for Ukrainian or foreign businesses).
19
An Enactment of Cabinet of Ministry establishes tariffs of USD $0.685 per
ton of oil transported through Ukrainian territory.
According to the Law on Licensing of Certain Economic Activities, the
transportation of crude oil and oil product via trunk pipelines is subject to
licensing. According to effective legislation, the executive body that grants
licenses for oil transportation is the National Commission for Energy
Regulation and the fee for a license is UAH 340. Licensing can serve as a
means of market entry restriction because the criteria for license granting and
annulment are too broad and in principle any business entity can be granted
or deprived its license at the convenience of the executive body.
Since oil is a strategic resource for most countries and their governments are
interested in the development of mutually beneficial relationships,
international treaties play an important role in the development of the oil
industry. Ukraine has concluded several agreements, mostly with Former
Soviet Union countries, that are designed to adjust tariffs or remove barriers
in oil transportation. These are:
1. Agreements with Turkey on the development pipe-line JeikhanSamsun. Ukraine and Turkey agreed to build a pipe-line and oil terminal as a
joint-venture. The joint-venture is granted tax and duty exemptions in Turkey
and Ukraine as well as other benefits.
2. Agreement with Kazakhstan on the coordination of activities related
to oil refining and petroleum production. The purpose of the agreement is
to coordinate activities in order to avoid mutual losses in petroleum and oil
industries. It achieves this goal by adjusting refining and extracting of oil, and,
if necessary, mutual oil supply. Oil included in the category of mutual supply
is exempted from duties and taxation.
3. Agreement with Russia on the cooperation in the development of oil
and gas industry of Russia. As a whole, the agreement consists of articles
that stipulate the possibility of mutually beneficial participation of Ukraine in
the discovery and extraction of oil deposits in Russia. It is mentioned that
20
resources devoted to this project are free of duties and taxation in both
countries.
4. Agreement with Uzbekistan on the participation in the development
of oil and gas industry of Uzbekistan. The agreement introduces basic
principles for the participation of Ukraine in oil extraction in Uzbekistan for
refining in Ukraine. In order to perform this task joint ventures will be created
and extracted oil is to be distributed according to the shares belonging to the
countries.
2.3. The mechanism of affecting the economy through oil shocks
The OPEC boycott in 1970s had a significant impact on OECD countries-its
all 24 countries (except Switzerland) experienced a slowdown of economic
growth after 1973:4. These countries suffered an increase in their inflation
rate at the same time. The fact that all countries were affected simultaneously
worsened the situation even more. The years 1973 to 1975, when the first
wave of oil price increase occurred, caused steep economic contraction and
jump in inflation (reaching 14% in 1974 on average)13. Then recession came
back, accompanied by increasing inflation, at the time of the second oil price
increase in 1979-1981. Employment was a casualty of the adjustment to the
oil shock, and unemployment rates approached the levels of World War Two,
with the peak of 9% in OECD countries on average. This was significantly
higher than in the USA, because of high degree of trade unionization in
Europe, which is positively correlated with real wage rigidity. In a result of
market clearing, Europe ended up with even greater increase in general price
level.
Below the economic explanation of these events is provided, with the
discussion of mechanisms how oil shock influences the major indicators of
economic performance .
Correlation between oil prices and the macroeconomic performance
13
From Organization for economic cooperation and development, Economic outlook, December 1989, Table R11-12.
21
Although there are a lot of researches which describe the relationship
between oil prices and major economic indicators, economists do not hold
the unique opinion what is the major reason which could explain the direct
and indirect effects. Despite the fact that impact on GDP is empirically
supported for many countries, it is difficult to build the correct mechanism
which could explain this relationship with other economic indicators. One
possible explanation of the great number of economic solutions is that
economists do not take into account monetary and fiscal policies conducted
by the government. In fact, these policies could serve as a powerful tool to
smooth the dependence of the country on the oil price fluctuations. But their
effect could differ depending on whether oil shock is anticipated or not.
Oil price increase can affect the macroeconomic behavior through mane
reasons.
First, there will be a transfer of income from oil-importing countries to oilexporting ones. This will reduce the aggregate demand because the demand
for oil will likely decline. As the propensity to spend of energy consumers will
be larger than the propensity to spend of those who gain income from
increased prices, the fall in demand is inevitable. Empirical study conducted
by IMF (2000) show that in 2000 the world transfer of income from oil
importers to oil suppliers amounted $65 billion or 0,2 % of world GDP. They
found a clear distinction in the effect on developing and developed countries:
the countries in transition are more affected by higher oil price, transferring
their income to oil suppliers at the rate of 0,6% of GDP, while developed
countries – only 0,2%.
Second, there will be a rise in the cost of production of goods and services in
the oil-importing country. It implies that relative price of energy goods will
increase (ceteris paribus), decreasing manufacturer’s profits. The direct impact
is the likely decrease in the aggregate supply in the short-run under the
assumption that wages are relatively inflexible and capital stock is constant.
Third, as Carruth, Hooker and Oswald (1998) pointed out, oil price increase
could cause the rise in unemployment. Their reasoning is based on the cost
structure of the firm. When the costs of production rise, employers will likely
22
pay lower wages, assuming that the market is competitive. In the labor supplydemand framework the labor supply decreases since some workers do not
agree to work for lower wage. So, structural unemployment rate temporarily
increases, but the long-run effect is ambiguous.
Fourth, the impact of oil prices on the inflation also can take place. Higher
energy prices can cause the decline in real income of the individuals. They
want to offset these losses through the increase in wage, and producers want
to restore their profits. In this case oil shock can cause the Central Bank to
tighten its policy, which in turn is connected with inflation. The overall effect
depends on the consumer’s expectations and the bias of the CB’s policy.
Fifth, the change in relative prices creates incentives to for oil-exporting
countries to increase oil extraction and investment. At the same time oilimporters will face lack in recourses and its investments could decrease. This
will change depending on the expected duration of price increase.
Finally, there will be direct and indirect effect on the financial market and
exchange rate. Through the mechanism described above, oil price increase
can affect equity and bond prices and the exchange rate. In the IS-LM
framework the possible response of oil chock by government is to increase
money supply. Thus LM curve shifts to the right and the new equilibrium
interest rate will be lower. Capital goes abroad, depreciating domestic
currency and improving trade balance.
Summing up this section, it can be shown that crude oil price increase can
cause a decline in GDP and a rise in unemployment. Indirectly it can affect
the overall price level and the currency exchange rate. However, these
findings depend on whether the shock is anticipated by consumers and the
government, and on the duration of expected increase. The effect of oil price
decrease can cause the greater effect on the economy. As Hooker (1996) and
later Hamilton (2000) found out, in case of permanent energy price decrease
the importers will likely face a rapid growth in GDP (ceteris paribus) due to
increase in investment jump in order to satisfy the demand for domestic
products through the widening of the capital stock.
23
For the case of Ukraine oil shock should matter, because of its great
dependence on the energy. Visual inspection of the series of GDP and prices
for oil in Ukraine allows assuming the negative correlation between these two
variables14.
Figure 2.5. Real GDP and the price of oil in Ukraine, 2001
1
2
3
4
5
6
7
8
9
10
11
2001, month
Price of oil
Real GDP
Source: UEPLAC, UICE monthly reports, author’s calculations.
However, the sample period for this graph is small enough and we cannot
judge about the robust connection between these parameters over time. This
plot illustrates the negative correlation only over the last year when Ukrainian
real GDP was steadily increasing and the world average prices for oil were
decreasing. One striking feature here is that there is immediate response
rather than lagged. The model allows to capture this feature, since monthly
lags are used rather than quarterly ones.
Correlation between economic indicators and state policies.
In this section it is explained why fiscal and monetary policies do matter for
the Ukrainian macroeconomy and their temporary and permanent effects are
observed. The AA-DD framework simultaneously allows to observe the
effect of fiscal and monetary expansion on output, unemployment and
14
All observations of GDP series are multiplied by a constant to make it compatible with oil price series.
24
inflation. Let us start with the graph, which shows the effect of government
expenditures on the parameters of interest.
Assume that the full-employed economy starts at point 1. In the short-run
fiscal expenditures has an immediate effect on the output and the exchange
rate. The increase in government expansion causes DD curve to move right
to DD2. As a result, new short-run equilibrium will be achieved at point 3,
with increased output and real appreciation of the domestic currency, due to
the fact that demand for domestic products is permanent. In the long-run
agents expect further appreciating of the domestic currency and demand for
money increases. Central Bank has to buy foreign reserves, thus decreasing
monetary base. As a result, AA curve shifts down and left to AA2 and the new
long-run equilibrium is achieved at point 2. Thus, appreciation “crowds out”
demand for domestic products, because they become more expensive relative
for imported products. The overall effect is the following: domestic currency
appreciated even further, and due to additional appreciation permanent fiscal
policy has no effect on output and employment. However, under temporary
fiscal expansion the economy will end up at point 3, due to the absence of
additional expectations. In this case output increases accompanied by
unemployment decrease.
Figure 2.6 The effect of temporary and permanent fiscal expansion
Source: Krugman Paul. R., Obstfield M. (2000); “International Economics: Theory and Policy”; 5th edition,
pp. 463.
25
Summing up this section, we developed the mechanism through which fiscal
policy can affect output, exchange rate and unemployment. In next chapter
we test the hypothesis whether the government expenditures cause changes in
the parameters of interest in AA-DD framework could be applied for the
Ukrainian case.
Under the monetary policy the overall effect on the GDP and unemployment
is similar. Figure 2 represents the mechanism of affecting the output and
exchange rate as a result of permanent and temporary monetary expansion.
Figure 2.7 The effect of temporary and permanent monetary expansion
Source: Krugman Paul. R., Obstfield M. (2000); “International Economics: Theory and Policy”; 5th edition,
pp. 462.
Again, let us assume that the full-employed economy starts at point 1 on the
graph. Temporary increase in money supply causes AA curve to move
upward to AA3 due to the requirements of asset market equilibrium. Thus,
similar to fiscal policy expenditures, in this case output increases and
unemployment reduces. The difference is that under monetary expansion
domestic currency depreciates. Moreover, the increase in monetary base
causes inflation jump.
Under the permanent monetary expansion, the demand for labor increases
since more labor is required. In conjunction with steadily increase in the price
26
level, wages are increased since employees have to work overtime. As a result,
producers raise prices for their goods and services.
Domestic goods become more expansive relative to foreign goods, and the
trade balance worsens.
In the graph AA first shifts upward to AA2 and the economy achieves shortrun equilibrium at point 2. Due to rise in domestic prices DD curve shifts to
the left to DD2 and real money supply reduces. Finally, AA curve shifts back
from AA2 to AA3. These two curves intersect at full-employment level due to
“overshooting effect”.
The overall result is the following: permanent monetary shock has a sharper
effect on output and employment relative to temporary one due to exchange
rate pessimistic expectations. Then economy returns to its full employment
position (point3), because all money prices rise in proportion with the
increase in monetary base.
Summary of findings:
Under the temporary fiscal expenditures output increases, domestic currency
appreciates, unemployment decreases, no effect on inflation.
Under the permanent increase in government expenditures the economy
returns to its initial full employment position because agents expect further
appreciation of the domestic currency. Output stays the same as well as
unemployment level. No direct effect on inflation.
Under the temporary increase in the monetary base output increases,
unemployment rate decreases, since more labor is now required. Domestic
currency depreciates. Inflation rate increases.
If the permanent expansion takes place, the impact on the indicators of the
interest is stronger: in the short-run output increases even more, inflation rises
significantly. Due to further depreciation, trade balance improves, but in the
long-run the economy returns to its initial position with slightly depreciated
domestic currency. Thus, in the long-run the AA-DD framework predicts the
neutrality of the monetary policy.
27
Since in the regression we use monthly values with the four maximum
number of lags rather than quarterly ones we cannot capture long-run affects
of both policies. On the other hand, it allows to analyze the short-run impacts
on GDP, inflation and unemployment. The model allows to check whether
the theory could be applied for the Ukrainian macroeconomy.
28
Chapter 3
EMPIRICS AND RESULTS
In this chapter I provide the empirical basis for the economic analysis of the
possible oil shocks on the main economic indicators described in the previous
chapter. According to the theory, oil shocks matter for the economy as well as
monetary and fiscal policies. I am interested in the short-run effects of oil
price fluctuations on GDP, inflation and unemployment in Ukraine.
3.1. Data description
For this work monthly series are taken. They are: inflation, unemployment
rate, GDP, indicator of monetary policy (currency in circulation), fiscal policy
(government expenditures) and the price of the crude oil. The data set is
mostly provided by the National Bank of Ukraine (NBU), UEPLAC and by
the State Committee of Statistics (DerzhKomStat). Series of world oil prices
can be found at IFS Survey, while data for Russian oil prices is available at the
Ukrainian Interbank Currency Exchange (UICE). The sample for time-series
analysis includes 108 monthly observations starting from early the 1993 up to
2001:12. In order to estimate an impact on inflation I used data from 1995:1.
Actually, the data for the earlier period are available, but as could be explained
in Appendix 1 one cannot rely on this period since the presence of outliers
will yield spurious results. Visual inspection allows to conclude that it is more
appropriate to start the analysis from 1995 when the
series becomes
predictable.
3.2. Methodology
For the estimation of oil price importance I applied a reduced-form approach
proposed by Gisser and Goodwin (1986). I estimated three St. Louis-type
equations for investigating the changes in Ukrainian real GDP,
unemployment level, and inflation rate:
29
.
.
.
.
4
4
4
)
∆GDPt = α0 + ∆∑mi M t −i + ∆∑ fi F t−i + ∆∑oi POt−i + λ1ui + εt
i=0
.
i=0
.
i=0
.
.
4
4
4
)
∆ Inf t = α0 + ∆∑mi M t−i + ∆∑ fi F t−i + ∆∑oi POt−i + λ2ui + εt
i=0
.
∆Un = α + ∆
t
0
4
i=0
.
mM
∑
i=0
i
4
t −i
+ ∆∑
i=0
.
fi F
4
t −i
i=0
.
)
+ ∆∑oi POt −i + λ3ui + ε t
i =0
where:
GDP- gross domestic product at 1990 constant rubles. Billions of UAH.
Seasonally adjusted.
Un- unemployment rate. Calculated as a percentage of Ukrainian labor force.
Seasonally adjusted.
Inf - Inflation rate. Calculated as the percentage change in consumer price
index to the previous month.
Regressors:
M- money stock (M1). Billions of UAH. Seasonally adjusted.
F- fiscal activity. Budget expenditures. Billions of UAH. Seasonally adjusted.
PO- real price of crude oil. Producer price index. USD/bbl.
)
ui - error correction term
Similar to many researches in this field, all the series are taken in the growth
rates, which allow to avoid the problem of variables incompatibility. The basic
ADF test for the obtained series indicates that all series are stationary in first
differences (see Appendix 2). So, in the model I used differences of the
growth rates rather than absolute. At the first stage OLS estimation is
performed, which allows to obtain the most precise estimators in case if all
conditions are satisfied (are presented later) and then check for
autocorrelation, heteroskedasticity and residuals normality. The approach to
use growth rates allows to use world average oil prices as a proxy for Russian
oil prices (Urals). The problem is that this data is fully confidential and there is
no official statistics for the period of interest. However, there is a sample over
the last two years obtained from Ukrainian Interbank Currency Exchange. As
30
the first graph in Appendix 1 indicates, the series for world prices and
Russian prices are highly correlated. Urals on average is cheaper by 1.5-2
dollars per barrel. As for the prices of oil produced in Ukraine, they show no
systematic trend and cannot be computed on the basis of the world average
prices. This suggests the existence of the price regulation in Ukraine, which
significantly affects the producer’s prices in Ukraine.
Since I am interested in the hypothesis whether Russian oil prices can
affect the Ukrainian economy, I can use average world oil prices in growth
rates as a reliable source.
3.2. Analysis of the results
The results of ECM with Newey-West covariance & standard errors are
presented below.
Table 3.1. Summary statistics
Dependent variables
Regressors
Intercept
Oil
Oil (-1)
Oil (-2)
Oil (-3)
Oil (-4)
∑ Oil
Money
Money (-1)
Money (-2)
Money (-3)
Money (-4)
∑ Money
Fiscal
Fiscal (-1)
Fiscal (-2)
Fiscal (-3)
Fiscal (-4)
∑ Fiscal
Resid
R-squared
DW statistics
*
GDP
Inflation
Unemployment
0.002 (0.01)
-0.032 (0.01)*
-0.074 (0.05)
-0.006 (0.00)*
0.002 (0.00)
-0.016 (0.005)*
-0.126 (0.07)**
-0.170 (0.1)***
-0.020 (0.27)
0.075 (0.29)
0.137 (0.28)**
0.211 (0.19)*
0.233 (0.11)*
-0.018 (0.01)*
0.009 (0.01)
0.077 (0.04)**
0.022 (0.01)*
0.031 (0.02)
0.121 (0.07)***
-0.372 (0.04)*
0.84
2.06
0.000 (0.01)
0.176 (0.05)*
0.002 (0.06)
0.001 (0.07)
0.040 (0.05)
0.032 (0.04)
0.276 (0.23)
-0.388 (0.10)*
0.510 (0.09)*
-0.212 (0.09)**
0.340 (0.09)*
0.311 (0.09)*
0.561 (0.14)*
0.13 (0.03)*
-0.081 (0.05)
-0.050 (0.07)
-0.056 (0.05)
-0.018 (0.03)
-0.069 (0.05)
-0.484 (0.04)*
0.78
2.15
-0.0038 (0.014)
-0.0046 (0.005)
0.00179 (0.006)
-0.00435 (0.004)
0.00591 (0.004)
0.00927 (0.004)**
0.007989 (0.007)
0.000021 (0.00)
-0.000085 (0.00)**
0.000014 (0.00)
0.000038 (0.00)
0.000005 (0.00)
-0.000006 (0.00)
-0.0000017 (0.005)
0.0000003 (0.00)
0.000029 (0.00)***
0.0000016 (0.00)
0.0000262 (0.00)**
0.000056 (0.00)
-0.6808 (0.196)*
0.53
2.05
significant at 1 % confidence level
31
** significant at 5 % confidence level
*** significant at 10 % confidence level
The table represents the results of four independently estimated St. Louis –
type equations. All series are taken in differences of the growth rates up to
2001:1215. The columns contain the impacts of 1% monthly change in growth
rates of the regressors on real GDP, inflation and unemployment rate.
Standard errors of the coefficients are given in the parentheses.
The method used is error correction mechanism (ECM) with Newey-West
standard errors. This technique is possible only under conditions that all series
are nonstationary in levels (growth rates in this case) and integrated of the
same order . First, simple OLS regression in levels is run and residuals are
obtained. This cointegrating regression is run without any lags which shows
the existence of long-run relationship between the variables. The necessary
condition for cointegration is that the residuals from the abovementioned
regression are white-noise, i.e. u~N(0,δ2). In other words they should be
stationary in levels. The Augmented Dickey-Fuller tests for residuals obtained
from the preliminary regressions indicate that the null hypothesis of a unit
root is rejected for all three regressions. Then the regression with all variables
in first differences and computed residuals from cointegrating regression is
run. This implies that all variables are cointegrated and there is a long-run
relationship between the variables, but it does not necessarily mean that this
holds for the short-run. The presence of cointegration between nonstationary
variables indicates that their stochastic trends are linked, that is they move in
the same direction. The dynamic path of cointegrated variables includes the
information about the deviations from the equilibrium that is why VAR
model, which does not capture this relationship, will lead to the specification
error. By contrast, the ECM corrects the relationship between variables in
differences and deviation from equilibrium. In other words, error correction
model allows for the short-run deviation of variables from their common
movements, but the economic mechanisms always correct these deviations.
15
Growth rates are calculated by the formula: (Xt -Xt-1)/Xt-1 which approximately equals ln (Xt/Xt-1) as
∆t→0
32
The obtained coefficient for the error correction mechanism (“equilibrium
error” or “speed of adjustment”) should be negative and less than unity,
which indicates the model stability. One can treat this coefficient as a
mechanism, which ties the short-run behaviour to its long-run value. If it is
statistically significant it shows what proportion of the disequilibrium in
dependent variables in one period is corrected in the next period.
For example, in the first regression (GDP) the estimated error correction
(RESID) is –0.372 (significant at 1% of confidence). This means that 37% of
the discrepancy between the actual and the long-run, or equilibrium, value of
real GDP is eliminated or corrected each month.
The same holds for
inflation and unemployment rates (-0.484 and –0.681 respectively).
The error correction model is a nonlinear, although in fact it is intrinsically
linear and can be deduced simply from the unrestricted form. That is why all
series are taken in growth rates. As Davidson and MacKinnon (1993) showed,
OLS estimates will be consistent and the problem of spurious regression
disappears. As an alternative to ECM, VAR estimation is possible. However, I
choose error correction model due to several reasons. First, VAR is more
suitable to make for predictions, which are not the main object of my
research. Second, in case the variables are of the same order of integration
and cointegrated, the error correction model is the best tool and must be
employed, while VAR is used when this is not the case.
In short, the construction and application of ECM requires the following
steps:
1. First of all, series are checked for stationarity. If they are nor
stationary, check the order of integration.
2. All series should be integrated of the same order.
3. This allows to run a cointegration regression and obtain the residuals
4. Residuals should be white-noise.
5. Run regression in differences with obtained residual series.
In my case all variables are stationary in first differences (in first differenced
growth rates), so they are integrated of order one I(1). The inspection of the
33
residuals indicates that they are stationary. So, ECM can be applied in this
case.
Summing up this section, it can be concluded that the error correction model
(ECM) has the following properties:
•
Shows both short-run and long-run aspects of variables dynamics
simultaneously;
•
Avoids the problem of spurious connection ;
•
Does not require preliminary distinction of variables between
exogenous and endogenous;
•
Satisfies all classical econometric assumptions.
As expected, all oil price coefficients have negative signs, with almost all
significant at significant levels. The sum of five monthly coefficients is
negative and statistically significant at 5% level. It means that 1 percentage
point difference in the growth rates of oil price will cause the 0.126
percentage points decrease in the difference of the real GDP growth rates.
Unfortunately, it is relatively hard to transform this figure into levels, so only
an approximation calculation could be made. Taking into account that oil
shocks cause the peak of their impact in a year (Hamilton, 2000; Hooker,
1996), this decrease in real GDP is significant enough.
It does not necessarily mean that real GDP will fall during its growth period: a
better explanation is that GDP will still rise, but at slower rate .The quarterly
coefficient can be interpreted and compared with IMF result under the
assumption that real GDP growth rate in previous period was zero.
I want to observe the quarterly impact of a 20% increase in the price of oil on
real and nominal GDP. For example, in fourth quarter of 2001 the Ukrainian
real GDP growth constituted 10.0 %. According to my estimation results, a
20% increase in oil price will likely decrease the quarterly growth rate of GDP
by
2,5%.
This
implies
that
“potential”
growth
rate
could
be
10%+2.5%=12.5% which will increase GDP in the fourth quarter by 0.46
bln of 1992 constant rubles or just 2.26 % of quarterly GDP. This will be
approximately UAH 1122 mln in nominal terms, or $211 mln. However, this
34
figure cannot be compared directly to that obtained by IMF ($505), because I
investigated only the first quarter impact, but IMF studied the first round
impact which could be rather broad. In my regression I used a larger number
of lags and found out that oil was still producing a negative impact on GDP (
although insignificant ). This indicates that the overall effect on GDP could
be larger and the simulated figure could increase substantially. The obtained
loss is approximately the same each period in 2001; this is also true for the
earlier periods. Hence, I can draw a conclusion that as a result of a 20%
increase in the price of oil, Ukrainian GDP will likely face $215 loss on
average as a first quarter impact (ceteris paribus). Of course, this result is
ambiguous, but it gives an approximate interpretation of estimated
coefficients.
An interpretation for the monthly coefficients is the following: assume that in
January price of oil has increased by 10 % (from 20 to 22 dollars per barrel,
for instance); immediately it will cause the real GDP growth to decrease by
0,32%. In March this figure will reach its peak at 0,06% and then the impact
of price increase gradually disappears – in May the decline in GDP growth
will be 0,16 %.
Meanwhile, monetary policy has the largest impact among all regressors,
starting its positive impact from the second lag and then increased next two
months. Its coefficients are 0,137 and 0,211 respectively. This trend also holds
if the larger number of lags is included. One striking result is that the
contemporaneous impact of the monetary policy is negative and statistically
significant. That could be explained by its slow effect which means that the
current policy decisions of National Bank are not affected by the
contemporaneous processes in the economy, but rather are predetermined by
the
past values of economic variables that seems preferable in the
circumstances of Ukraine. However, the sum of five monthly lags is positive
and statistically significant (0.233), indicating that the first quarter impact does
cause changes in real GDP.
The same explanation could be appropriate for the fiscal policy, which
contemporaneously causes a decline in GDP growth rate. Again, fiscal policy
35
starts working from second lag and the sum of its coefficients is positive, as it
should be, and significant at 10% level. Similar to Gisser and Goodwin’s
findings, all coefficients starting from second lag have expected signs. For the
case of the United States, the lagged fiscal policy does not affect real GDP,
while for the Ukrainian case it matters, but its effect is not as large as that of
monetary policy. It could be explained by the fact that these countries have
different markets and macroeconomic policies. In the USA the objectives of
the Federal Reserve are well-known and markets are functioning efficiently; in
Ukraine people do not anticipate policy changes. I can draw a conclusion that
among the regressors real price of oil has immediate impact on changes in real
GDP, while fiscal and monetary base start to affect domestic product after
some period. Gisser and Goodwin concluded that fiscal policy did not effect
US GDP, but this discrepancy could be explained by different regulatory
policies in both countries.
Adjusted R-square of the regression is 0.84, so about 84% of changes in GDP
growth are explained by lagged regressors. F-statistics is rather high and the
null of no joint significance is strongly rejected at 1% of confidence. The
correct specification of the regression is also confirmed by low S.E. of the
regression (5%) and by omitted variable test, which does not reject the null of
no omitted variable (P-value=0.32).
Basic diagnostic statistics (see Appendix 3) indicate the normality of the
residuals. Thus, Breusch-Godfrey Serial Correlation LM Test does not rejects
the hypothesis of no serial correlation with P-value=0.175. This decision rule
is also supported by Durbin-Watson statistics which equals 2.06 and is very
close to 2. White Heteroskedasticity test indicates that residuals are
homoskedastic (P-value=0.29). The condition that residuals are normally
distributed is confirmed by Jarque-Bera test. This can also be seen on
CUSUM and CUSUMQ diagnostic figures.
In order to avoid the problems of autocorrelated disturbances and
heteroskedasticity, the regression with Newey-West standard errors is applied.
This method provides a way to calculate consistent covariance matrices in the
presence of both heteroskedasticity and serial correlation. As an alternative,
36
the regression with robust standard errors was applied, which gave the same
coefficients and approximately the same errors as the N-W approach. Under
the second method the null of no serial correlation is also supported by
Breusch-Godfrey Serial Correlation LM Test
So far, the validity of the results is confirmed by basic tests, so we can rely on
it in order to make econometrically valid judgments about the relationship of
oil price and basic economic indicators. The assumption of normally
distributed disturbances holds for other regressions as well (see Appendices 45) for details. This allows to use OLS estimator, which is known to be the
most efficient among the others.
The results for inflation are quite different. Here oil prices and monetary
policy do cause changes in CPI. Crude oil prices are significant only in the
same month, indicating that increase in input price causes a jump in the
difference of inflation growth rates. It implies that oil price immediately
causes increase in inflation. All oil lagged values have expected positive sign,
but not significant, however. The sum of the coefficients indicates that 1%
change in oil price growth rates will increase quarterly growth of inflation by
more than 0,27%, but it is not significant either.
According to the theory, monetary policy has the largest effect on inflation,
reaching its peak in the third lag (0.340). The results show that all monthly
coefficients are statistically significant, but some of them have positive sign.
Nevertheless, the sum of coefficients is positive and significant which means
that monetary policy is positively correlated with inflation in Ukraine. Fiscal
policy turns out to be insignificant except its for the contemporaneous value,
and as discussed in previous chapter, it is positive.
Again, various tests prove the absence of autocorrelation as well as
heteroskedasticity. R-squared of the regression is rather high - 0.78 % which
could be treated that Ukrainian inflation is explained by changes in the
regressors only by 78 %. This can indicate about the correct model
specification, which is also supported by almost zero probability of Fstatistics.
37
For changes in the growth rates of unemployment all the coefficients are
actually zero, although significant in all lags. Here oil prices start affecting
unemployment only from the fourth lag and this coefficient is positive and
significant. Taking into account its small value, no credible conclusions about
the correlation between oil prices and unemployment rates could be done.
Similar conclusions can be made for both monetary and fiscal policies.
Moreover, the power of the regression is the lowest among the others (R2
=0.53). Again, this could be explained by a weak dependence of
unemployment on the regressors for the case of Ukraine.
38
Chapter 4
DISCUSSION
To answer the question whether oil price fluctuations could influence the
Ukrainian macroeconomy, the paper deals with three St.Louis-type
equations, where oil prices simultaneously with monetary and fiscal
policies influence real GDP, inflation and unemployment. All series were
taken in the growth rates in order to smooth the fluctuations and obtain
efficient estimates.
The results based on Error Correction Model with Newey-West
covariance and standard errors suggest that I cannot reject the null of no
effect on the Ukrainian macroeconomy with different significant levels. I
employed the estimation using four monthly lags of the regressors and
found out that oil prices matter for GDP and inflation, but I failed to find
any significant effect on unemployment. All significant coefficients have
expected signs.
For the case of real GDP, fiscal policy starts its effect only from second
lag. It is argued in the literature that countries in transition in order to
avert inflationary consequences should decrease government expenditures.
In 2001 Ukraine faced a significant decrease in inflation, ending up with
annual rate equal 6.1% (25.8% in 2000). Investigating the government
expenditures plot it could be seen that it was still growing, but a slower
rate. Monetary policy turned out to affect domestic product starting from
third lag and then its effect gradually increases if the larger number of lags
is introduced. Oil price fluctuations are significant almost at all lags and
show negative trend if the longer period is investigated. The economy will
not likely return to its initial position after this period, because general
price level will not probably stay the same, so the effect is not temporary.
Unfortunately, the investigated period is not long enough to make any
credible conclusions about the long-run. If oil price changes relative to the
39
costs of other inputs are long-term, then the long-run effects will occur in
the production function as producers choose different inputs based on
their prices. In the short-run, the Ukrainian GDP is affected, but the
magnitude of its decrease depends on the policy conducted by authority.
The Ukrainian authority is rather flexible in terms of its responses to oil
price shocks, if there is a threat of more significant losses than only
decrease in tax collections. In order to maintain the production at its
current level and to gain as much tax collections as possible from oil
importers, it permanently changes the import duties and excise taxes as
well as taxation schemes for energy consumers. In conjunction with the
policy conducted by Russian government, the overall effect could cause
GDP growth increase again. In early August 1997, due to crisis in Russia
prices for gasoline in Ukraine immediately jumped twofold and continued
to rise because of consumers’ pessimistic expectations. Many farmers were
not able to gather the harvest because they could not afford diesel fuel at
high prices. As a response, Ukrainian government immediately annulled
the import duty for diesel fuel from Russia. From the trade-off point of
view, this decrease in budget contributions from positive duty is more
preferable, than potential losses in agricultural sector. The price of crude
oil also depends on the policy conducted by Russian government. For
example, it permanently changes export duties in order to maintain
contributions to the budget. Up to January 2002, this duty aggregated € 27
per ton. Detailed scale of duties is presented below.
Table 4.1 The scale of Russian export duties for crude oil
Price of oil, $/bbl
<12.5
12.5-15
15-17.5
17.5-20
20-22.5
22.5-25
25-27.5
27.5-30
30-32.5
>32.5
Export duty, €/ton
0
2
5
9
14
20
27
34
41
48
Source: http://oilreview.kiev.ua
40
On May 2, 2002 Russian government announced that the export duty will
be decreased in June to € 8 per ton because of oil overproduction in the
country. For Ukraine it means that demand for Russian oil could increase,
that in turn could cause increase in GDP (ceteris paribus). Next, there are
three Russian oil companies which operate in Ukraine and which currently
involved in the trade war. After the OPEC request to reduce oil
production and export in late 2001, the domestic price for crude oil in
Russia fell dramatically reaching a price of $5 per barrel. Four out of six
Ukrainian refineries work with Russian oil and belong to Russia which
allows to export oil at the price, which is much lower then world one. On
the other hand, they sell refined products at competitive prices thus
making enormous profits. The major role in price setting plays TNK
which owns about 64 thousand filling stations all over Ukraine. One
important thing here is that costs of production of one unit of petrol by
domestic oil refineries are rather high and Ukraine cannot not compete
with Russia. I guess that corruption can take place and in fact price for
imported oil is much lower than officially announced. Extra profits (since
demand does not fall)16 allow running dumping policy. The overall scheme
is: competing Russian oil companies respond to world increase in oil price
and raise price for their products to make more profits. Then, in order to
capture the market, they lower the price again, but the economy never
returns to its initial condition.
This could also explain the immediate effect of oil prices on inflation. Due
to lowering the price of oil, Ukrainian producers do not raise their prices
next period; in conjunction with the monetary policy, inflation jump effect
could be nullified.
As a result of oil export reduction by OPEC countries and by Russia, the
world average price for crude oil in first quarter of 2002 jumped again to
$24-26 per barrel. During this period Ukrainian refineries faced a decrease
in production and the prices for gasoline slightly increased. Unfortunately,
7
see figure 2.1
41
data for inflation and real GDP were not available, so this period is not
included into the research. One of the reasons why Ukrainian response to
oil price increase was not significant is that in the first quarter the general
slowdown in economic activity was observed (this also holds for other
countries). Oil price increase could have more significant impact in spring,
during the sowing campaign.
There could be several explanations why unemployment is weakly
affected. First, it is recognized that official unemployment rates are
underestimated and the real rates are much higher. Unfortunately, it is not
possible to calculate reliable rates at this stage. However, the results could
make sense under the assumption that real and official unemployment
series are highly correlated. Since all the values are taken in the growth
rates rather than values, the estimates are reliable. Second, weak
unionization of Ukrainian labor market does not allow to keep real wages
constant through the wage indexation or to support long-term contracts
with rigid wages. “Ukrprofspilka”- trade union inherited from the Soviet
era cannot achieve all the goals due to variety of reasons. Third, the share
of workers employed in other sectors is higher than the share of labor
force involved to the energy-consuming sector , so the impact of higher oil
prices could be “smoothed” by neutrality of a latter. Fourth reason is wage
arrears. The reasoning is based on the assumption that Ukrainians do not
quickly respond to the cuts in their real wages. The firm’s cost-benefit
analysis suggests that the possible response to oil price increase is to
reduce expenses either by firing employees or by cutting real wages. It
could be achieved by not paying wages in time – inflation will make them
less. Wage arrears is a common practice in Ukraine and its total volume
has been increased up to the late 2000 (UAH 575 mln in 1995 versus 6325
mln in the second quarter of year 2000)17. I guess that the Ukrainian
economy is not in its full employment level and workers are afraid to
become unemployed if they leave their jobs in a response to wage
17
UEPLAC “Ukrainian Economic Trends”, January 2001.
42
reduction, taking into account the competitiveness of labour market.
Today the average vacancy ratio which is calculated as the number of
registered job seekers to the number of vacancies is fluctuating at the
range of 10-20 persons per vacancy18. This implies that labor market is
highly competitive and is not very sensitive to wage cuts due to the
reasons described above. This idea is also supported by my finding that
monetary and fiscal policies do not cause significant changes in short-run
unemployment level as well.
There could be several ways how government could cope with oil shocks
consequences. First is monetary policy which is considered to be the best
tool. An increase in the growth rate of money supply has two effects. First,
added liquidity should reduce nominal interest rate and cause domestic
currency depreciation. The latter is a positive change which can improve
trade balance; hence, more oil will be supplied. Second is that it rise in
money supply may cause more rapid inflation which increases nominal
interest rate. Most economists agree that, for a modest and unanticipated
monetary expansion, the liquidity effect would dominate. The National
Bank of Ukraine (NBU) should also take into account the inflationary
expectations of the consumers while running expansionary monetary
policy. On the other hand, increase in the growth rate of money supply
may cause some drawbacks. As Hamilton (2001) found out, the potential
of monetary policy to avert the contractionary consequences of oil shocks
is not as great as suggested by economists. He argues that in some cases
the authority should ignore them. Hamilton bases this idea on the Gertler
and Watson’s (1997) estimation that the biggest effect of an oil shock do
not appear until three of four quarters after the shock. If this is the case
for Ukraine, the NBU’s increase in money supply will only cause additional
jump in inflation. This point of view is also supported by Hunt, Isard and
Laxton (2001) who state that delay in monetary policy response could be
more appropriate in some cases.
18
ibid
43
Second, negative consequences of oil price increase could be smoothed by
decreasing import tariffs. In this case tariff policy should be oriented to
attract as much oil inflow as possible. A best example of such policy was
presented above when Government set import duty rate equal zero in a
response to oil shock. This move came into effect after some period average price for gasoline decreased, but did not return to its initial level.
Also, temporary tax relief for oil refineries could be a solution. In this case
they will be able to supply cheaper refined products.
Finally, in anticipation of oil shock the increase in oil strategic reserves
could be helpful. For example, in a response to OPEC’s decision to cut
production in first quarter of 2002, the U.S. decided to increase its
strategic reserves by 10%. Now U.S. oil stock is enough to maintain
current consumption level for 30-35 days. Ukraine has two large oil
terminals, situated in Odessa and Feodosia (Crimea); their total capacity is
enough to ensure the economy with oil for some period.
The government should also conduct sustainable policy in respect of oil
products market. It should resign from “seasonal” switching of policies, as
it jeopardizes oil market stability and exchange rate.
However, it is difficult to judge about the relationship between oil prices
and the macroeconomy on the basis of all assumptions made. There could
be some other powerful factors, which could matter. The purpose of the
paper is to show the direct impact of oil price fluctuations on the basis of
economic analysis under some assumptions, ignoring the hidden factors.
44
CONCLUSION
So far, I found a clear correlation between oil price fluctuations and such
indicators of economic performance as GDP and inflation. One percent
increase in the growth rate of real oil price will likely decrease next quarter’
GDP growth rate by 0.126 percentage points and its effect is still increasing
after. The results are consistent with those obtained by IMF (2000) and have
strong predictive power. The impact on unemployment level is not significant
which could be explained by weak reactions of Ukrainian workers to the wage
reduction. The results also suggest that economic theory could be applied for
the case of Ukraine in order to see short-run impact on main economic
indicators. A deeper investigation of both the theoretical and empirical
aspects of the results could be an interesting topic for further research.
45
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48
APPENDIX 1
Comparative prices of oil, UAH/bbl (2000-2001)
200,00
UAH/bbl
150,00
100,00
50,00
20
20
00
M
7
00
M
1
20 0
01
M
20 1
01
M
20 4
01
20 M7
01
M
10
M
4
00
20
20
00
M
1
0,00
OPEC
Urals
Ukrainian oil
Source: UICE, IFS, NBU, author’s calculations
Inflation in Ukraine, %
100
80
60
40
20
Source: UEPLAC
49
2001M10
2001M3
2000M8
2000M1
1999M6
1998M11
1998M4
1997M9
1997M2
1996M7
1995M12
1995M5
1994M10
1994M3
1993M8
-20
1993M1
0
APPENDIX 2
Augmented Dickey-Fuller Tests for Stationarity
Fiscal, levels
ADF Test Statistic
-3.109170
1% Critical Value*
5% Critical Value
10% Critical Value
-4.0521
-3.4548
-3.1528
-5.515130
1% Critical Value*
-4.0530
-2.514162
1% Critical Value*
5% Critical Value
10% Critical Value
-4.0560
-3.4566
-3.1539
-4.846936
1% Critical Value*
-4.0591
-3.146504
1% Critical Value*
5% Critical Value
10% Critical Value
-4.0540
-3.4557
-3.1534
-6.408585
1% Critical Value*
-4.0550
-3.187810
1% Critical Value*
5% Critical Value
10% Critical Value
-4.0535
-3.4057
-3.1514
-5.163239
1% Critical Value*
-4.0560
-3.307849
1% Critical Value*
5% Critical Value
10% Critical Value
-4.0928
-3.4739
-3.1640
1% Critical Value*
-4.0948
1% Critical Value*
5% Critical Value
10% Critical Value
-4.0540
-3.4557
-3.1534
1% Critical Value*
-4.0550
Fiscal, first differences
ADF Test Statistic
Money, levels
ADF Test Statistic
Money, first differences
ADF Test Statistic
Price of oil, levels
ADF Test Statistic
Price of oil, first differences
ADF Test Statistic
GDP, levels
ADF Test Statistic
GDP, first differences
ADF Test Statistic
Inflation, levels
ADF Test Statistic
Inflation, first differences
ADF Test Statistic
-5.836822
Unemployment, levels
ADF Test Statistic
-2.601341
Unemployment, first differences
ADF Test Statistic
Notes:
•
•
•
-5.598163
All series are taken in the growth rates
The number of lags was takes in accordance with LM statistics
Reject the null hypothesis of the Unit Root if ADF statistics exceeds the
MacKinnon critical value which means that is all series are stationary in first
differences
50
APPENDIX 3
GDP regression. Residuals diagnostic tests
Breusch-Godfrey Serial Correlation LM Test:
F-statistic
Obs*R-squared
1.104572
16.35798
Probability
Probability
0.375689
0.175384
1.181581
35.87168
Probability
Probability
0.287186
0.291721
White Heteroskedasticity Test:
F-statistic
Obs*R-squared
14
Series: Residuals
Sample 1993:08 2000:12
Observations 89
12
10
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
8
6
4
2
-7.94E-19
0.004989
0.347297
-0.330144
0.121066
0.041193
3.292430
Jarque-Bera
Probability
0.342289
0.842700
0
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
Ramsey RESET Test:
F-statistic
Log likelihood ratio
0.796558
0.992943
Probability
Probability
30
20
10
0
-10
-20
-30
1995
1996
1997
CUSUM
1998
1999
2000
5% Significance
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
1995
1996
1997
1998
C USU M of Squares
1999
2000
5% Significance
51
0.375139
0.319024
APPENDIX 4
Inflation regression. Residuals diagnostic tests
Breusch-Godfrey Serial Correlation LM Test:
F-statistic
Obs*R-squared
0.810642
1.958230
Probability
Probability
0.448103
0.375643
0.903280
28.27187
Probability
Probability
0.611487
0.556032
White Heteroskedasticity Test:
F-statistic
Obs*R-squared
10
Series: Residuals
Sample 1995:02 2001:11
Observations 94
8
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
6
4
-0.000173
0.006082
0.208546
-0.167080
0.068719
0.028551
3.243839
2
Jarque-Bera
Probability
0.245647
0.884420
0
-0.15 -0.10 -0.05 0.00 0.05
0.10
0.15 0.20
Ramsey RESET Test:
F-statistic
Log likelihood ratio
1.384358
6.890807
Probability
Probability
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.3
95
96
97
98
99
Recursive Residuals
00
01
± 2 S.E.
30
20
10
0
-10
-20
-30
95
96
97
98
CUSUM
99
00
5% Significance
52
01
0.247940
0.141772
APPENDIX 5
Unemployment regression. Residuals diagnostic tests
Breusch-Godfrey Serial Correlation LM Test:
F-statistic
Obs*R-squared
0.901050
13.54916
Probability
Probability
0.550569
0.330419
1.304002
40.36884
Probability
Probability
0.182157
0.209387
White Heteroskedasticity Test:
F-statistic
Obs*R-squared
12
Series: Residuals
Sample 1994:01 2001:11
Observations 95
10
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
8
6
4
2
-4.82E-18
-0.005980
0.150550
-0.147483
0.061300
0.132845
2.952371
Jarque-Bera
Probability
0
-0.15
-0.10
-0.05
0.00
0.05
0.10
0.288402
0.865714
0.15
Ramsey RESET Test:
F-statistic
Log likelihood ratio
0.707840
0.880705
Probability
Probability
30
20
10
0
-10
-20
-30
1996
1997
1998
CUSUM
1999
2000
2001
5% Sig nificance
1.2
1.0
0.8
0.6
0.4
0.2
0.0
-0.2
96
97
98
99
CUSUM of Sq uares
00
01
5% Significance
53
0.402801
0.348009